# Self-supervised Learning for Clustering of Wireless Spectrum Activity

arXiv: https://arxiv.org/abs/2210.02899 · HTML: https://mlsanity.com/paper/2210.02899/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 33 references that are in the map.
- First posted on arXiv 22 Sep 2022.

## Builds on (most cited first, 16 of 16)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [Deep Clustering for Unsupervised Learning of Visual Features](https://mlsanity.com/paper/1807.05520/) (2018)
- [Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey](https://mlsanity.com/paper/1902.06162/) (2019)
- [Over the Air Deep Learning Based Radio Signal Classification](https://mlsanity.com/paper/1712.04578/) (2017)
- [Deep Learning in Mobile and Wireless Networking: A Survey](https://mlsanity.com/paper/1803.04311/) (2018)
- [Distributed Deep Learning Models for Wireless Signal Classification with Low-Cost Spectrum Sensors](https://mlsanity.com/paper/1707.08908/) (2017)
- [Self-Supervised RF Signal Representation Learning for NextG Signal Classification with Deep Learning](https://mlsanity.com/paper/2207.03046/) (2022)
- [Electrosense: Open and Big Spectrum Data](https://mlsanity.com/paper/1703.09989/) (2017)
- [SAIFE: Unsupervised Wireless Spectrum Anomaly Detection with Interpretable Features](https://mlsanity.com/paper/1807.08316/) (2018)
- [Deep Predictive Coding Neural Network for RF Anomaly Detection in Wireless Networks](https://mlsanity.com/paper/1803.06054/) (2018)
- [The RFML Ecosystem: A Look at the Unique Challenges of Applying Deep Learning to Radio Frequency Applications](https://mlsanity.com/paper/2010.00432/) (2020)
- [Database-assisted Distributed Spectrum Sharing](https://mlsanity.com/paper/1301.2848/) (2013)
- [Crowdsourced wireless spectrum anomaly detection](https://mlsanity.com/paper/1903.05408/) (2019)
- [Radio Access Technology Characterisation Through Object Detection](https://mlsanity.com/paper/2007.13561/) (2020)

## Built on by (most cited first, 1 of 1)

- [XAI for Self-supervised Clustering of Wireless Spectrum Activity](https://mlsanity.com/paper/2305.10060/) (2023)

## Abstract (arXiv)

In recent years, much work has been done on processing of wireless spectrum data involving machine learning techniques in domain-related problems for cognitive radio networks, such as anomaly detection, modulation classification, technology classification and device fingerprinting. Most of the solutions are based on labeled data, created in a controlled manner and processed with supervised learning approaches. However, spectrum data measured in real-world environment is highly nondeterministic, making its labeling a laborious and expensive process, requiring domain expertise, thus being one of the main drawbacks of using supervised learning approaches in this domain. In this paper, we investigate the use of self-supervised learning (SSL) for exploring spectrum activities in a real-world unlabeled data. In particular, we compare the performance of two SSL models, one based on a reference DeepCluster architecture and one adapted for spectrum activity identification and clustering, and a baseline model based on K-means clustering algorithm. We show that SSL models achieve superior performance regarding the quality of extracted features and clustering performance. With SSL models we achieve reduction of the feature vectors size by two orders of magnitude, while improving the performance by a factor of 2 to 2.5 across the evaluation metrics, supported by visual assessment. Additionally we show that adaptation of the reference SSL architecture to the domain data provides reduction of model complexity by one order of magnitude, while preserving or even improving the clustering performance.